import torch
from torch import nn
import torch.utils
from d2l import torch as d2l # pip install d2l
from torch.utils.data import Dataset, DataLoader  
from PIL import Image  
import os  
from torchvision import datasets, transforms  

class Reshape(torch.nn.Module):
    def forward(self,x):
        return x.view(-1,1,28,28)

net = torch.nn.Sequential(
    nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2,stride=2),
    nn.Conv2d(6,16,kernel_size=5),nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2,stride=2),
    nn.Flatten(),
    nn.Linear(16*5*5,120),nn.Sigmoid(),
    nn.Linear(120,84),nn.Sigmoid(),
    nn.Linear(84,10)
)

def train_ch6(net,train_iter,test_iter,num_epochs,lr,device):
    def init_weights(m):
        if type(m) == nn.Linear or type(m)==nn.Conv2d:
            nn.init.xavier_uniform_(m.weight)
    net.apply(init_weights)
    print('train on', device)
    net.to(device)
    optimizer = torch.optim.SGD(net.parameters(), lr=lr)
    loss = nn.CrossEntropyLoss()
    # animator = d2l.Animator(xlabel='epoch',xlim=[1,num_epochs],legend=['train loss','train acc','test acc'])
    # timer,num_batches = d2l.Timer(),len(train_iter)
    num_batches = len(train_iter)
    # print(num_batches)
    for epoch in range(num_epochs):
        print('epoch:{}'.format(epoch),end='\t')
        metric = d2l.Accumulator(3)
        net.train()
        for i,(X,y) in enumerate(train_iter):
            # timer.start()
            optimizer.zero_grad()
            X,y = X.to(device),y.to(device)
            y_hat = net(X)
            # print(y_hat.shape)
            # print(y_hat)
            # print(y.shape)
            # print(y)
            # quit()
            l = loss(y_hat,y)
            l.backward()
            optimizer.step()
            with torch.no_grad():
                metric.add(l*X.shape[0],d2l.accuracy(y_hat,y),X.shape[0])
            # timer.stop()
            train_l = metric[0]/metric[2]
            train_acc = metric[1]/metric[2]
            # if (i+1)%(num_batches//5)==0 or i==num_batches-1:
            #     animator.add(epoch+(i+1)/num_batches,(train_l,train_acc,None))
        test_acc = evaluate_accuracy_gpu(net,test_iter)
        # animator.add(epoch+1,(None,None,test_acc))
        print(f'loss {train_l:.3f},train acc {train_acc:.3f},'f'test acc {test_acc:.3f}')

def evaluate_accuracy_gpu(net, data_iter, device=None): #@save
    """使⽤GPU计算模型在数据集上的精度。"""
    if isinstance(net, torch.nn.Module):
        net.eval() # 设置为评估模式
        if not device:
            device = next(iter(net.parameters())).device
    # 正确预测的数量，总预测的数量
    metric = d2l.Accumulator(2)
    for X, y in data_iter:
        if isinstance(X, list):
            # BERT微调所需的（之后将介绍）
            X = [x.to(device) for x in X]
        else:
            X = X.to(device)
        y = y.to(device)
        metric.add(d2l.accuracy(net(X), y), y.numel())
    return metric[0] / metric[1]



def run1():
    transform = transforms.Compose([transforms.Grayscale(num_output_channels=1),transforms.ToTensor()])  
    print('loading data ...')
    FashionMNIST_train = datasets.ImageFolder(root='datasets/FashionMNIST/train', transform=transform)
    FashionMNIST_test =  datasets.ImageFolder(root='datasets/FashionMNIST/test', transform=transform)
    batch_size = 60000
    train_iter = DataLoader(dataset=FashionMNIST_train,batch_size=batch_size,shuffle=True)
    test_iter = DataLoader(dataset=FashionMNIST_test,batch_size=batch_size,shuffle=True)
    lr, num_epochs = 0.9, 20
    train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
   
def run():
    batch_size = 256
    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
    lr, num_epochs = 1, 300
    train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())


if __name__ == '__main__':
    run()
